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Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model

Author

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  • Yalian Li
  • Hu Yang

Abstract

This paper is concerned with the parameter estimator in linear regression model. To overcome the multicollinearity problem, two new classes of estimators called the almost unbiased ridge‐type principal component estimator (AURPCE) and the almost unbiased Liu‐type principal component estimator (AULPCE) are proposed, respectively. The mean squared error matrix of the proposed estimators is derived and compared, and some properties of the proposed estimators are also discussed. Finally, a Monte Carlo simulation study is given to illustrate the performance of the proposed estimators.

Suggested Citation

  • Yalian Li & Hu Yang, 2014. "Two Classes of Almost Unbiased Type Principal Component Estimators in Linear Regression Model," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:639070
    DOI: 10.1155/2014/639070
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    References listed on IDEAS

    as
    1. Yalian Li & Hu Yang, 2010. "A new stochastic mixed ridge estimator in linear regression model," Statistical Papers, Springer, vol. 51(2), pages 315-323, June.
    2. Sarkar, Nityananda, 1996. "Mean square error matrix comparison of some estimators in linear regressions with multicollinearity," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 133-138, October.
    3. M. Hubert & P. Wijekoon, 2006. "Improvement of the Liu estimator in linear regression model," Statistical Papers, Springer, vol. 47(3), pages 471-479, June.
    Full references (including those not matched with items on IDEAS)

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